Next generation FKBP52 targeting drugs for the treatment of prostate and breast cancer

ABSTRACT

Procedures for inhibiting hormone receptor activation include administering to a subject in need of hormone receptor inhibition a compound having a chemical structure of a molecule that, when docked in the PPIase pocket, could disrupt proline-rich loop conformation and interactions. Procedures for treating prostate cancer or breast cancer include administering to a subject having prostate cancer or breast cancer a compound having a chemical structure of a molecule that, when docked in the PPIase pocket, could disrupt proline-rich loop conformation and interactions.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a utility conversion and claims priority to U.S.Ser. No. 62/963,873, filed Jan. 21, 2020, the contents of which areincorporated herein by reference in their entirety for all purposes.

This invention was made with government support under W81XWH-17-1-0435awarded by the Medical Research and Development Command. The governmenthas certain rights in the invention.

BACKGROUND INFORMATION 1. Field

The present invention relates generally to the field of medicine anddisease treatment. More particularly, it concerns methods of inhibitinghormone receptor activation and treating cancer.

2. Background

Androgens are a major stimulator of prostate tumor growth and allcurrent therapies act as classic antagonists by competing with androgensfor binding the AR (androgen receptor) hormone binding pocket. Thismechanism of action exploits the dependence of AR for hormone activationand current treatment options are essentially ineffective onceandrogen-dependence is lost. Thus, drugs that target novel surfaces onAR and/or novel AR regulatory mechanisms are promising additions for thetreatment of hormone refractory prostate cancer. Both FKBP52 andβ-catenin have emerged in recent years as attractive therapeutictargets. Applicant's previous patents describe MJC13, which represents afirst-in-class drug for targeting the regulation of AR by FKBP52.Through binding a recently identified regulatory surface on AR (BF3),MJC13 prevents the FKBP52-receptor complex from dissociating resultingin the retention of AR in the cytoplasm. MJC13 was shown to effectivelyblock AR signaling and AR-dependent cancer cell proliferation in avariety of human prostate cancer cell lines, and preliminary preclinicalstudies demonstrate impressive effects on tumor growth in a prostatecancer xenograft model.

Applicants previously described MJC13 as an inhibitor ofFKBP52-regulated AR activity (De Leon et. al. 2011. PNAS. 108(29):11878-83) by targeting the AR BF3 surface. Applicants have alsogenerated a large amount of preliminary data describing a novelmechanism by which FKBP52 and β-catenin interact to co-regulate ARactivity in prostate cancer cells. In addition, Applicants havepreliminary data demonstrating that MJC13 targeting to the AR BF3surface abrogates β-catenin interaction with AR (manuscript inpreparation). Our data show that the FKBP52 proline-rich loop iscritical for FKBP5243-catenin co-regulation of AR activity, and thatdrugs that disrupt interactions at the proline-rich loop wouldeffectively block FKBP5243-catenin/AR interactions. Applicants proposethat specific small molecules docked within the PPIase pocket can affectproline-rich loop conformation and interactions. Precedence exists forthis as FK506-binding to the FKBP12 PPIase pocket leads to areorientation of the FKBP12 proline-rich loop. Thus, Applicants electedto perform structure-based drug design to identify small moleculespredicted to target the FKBP52 PPIase pocket. Our previous, smallerscale in silico screens identified GMC1 as a hit molecule that displayedspecific inhibition of AR, GR and PR activity and Applicants patentedthis molecule for use in treating prostate and breast cancer. In themeantime, Applicants have performed a broader scale in silico screen toidentify the next generation of direct FKBP52 targeting drugs thatrepresent new chemotypes independent of GMC1. That broader in silicoscreening process and the new hit molecules identified are detailedbelow.

Prostate cancer is the most common cancer among men in severalcountries, which have presented 1.3 million new cases in 2018 alone¹.The chaperon proteins of the cancer patients facilitate both the dynamicprotein folding, unfolding, organization, and degradation throughATP-dependent cycles of binding and releasing for the protein'sfunction.²⁻³ One family of such chaperones are FK506-binding proteins(FKBPs); FKBPs and cyclophilins (CyP) belong to the immunophilin familythat are cellular receptors for immunosuppressant drugs such as FK506,rapamicyn and cyclosporine A (CsA).²⁻³ FKBPs exhibit peptidyl prolylisomerase (PPIase) activity and catalyze the cis/trans isomerization inprotein folding process in the cytoplasm, and have important roles ofprotein stability, protein trafficking, receptor signaling andothers.²⁻³

FKBP52 (also known as FKBP59/HSP56) is an immunophilin belonging to theFKBP family and is an important member of the inactive steroidreceptor/heat-shock protein 90 heterocomplex (HSP90) complex. FKBP52 isa positive regulator for binding of hormones to steroid hormonereceptors, which has been presented in studies⁴⁻⁶ by reporter geneassays in yeast and mammalian cells.²⁻³ In hormone-dependent prostatecancers, the level of FKBP52 expression is highly up-regulated comparedto the normal tissue.²⁻³ In addition, the immunophilin enhances theandrogen receptor actions of those therapies based on androgenexcision^(3, 7). Therefore, even though the androgen levels in theplasma are greatly reduced, the androgen can generate a response viaAR-HSP90 complexes.⁴ Studies of human prostate biopsies revealed thatFKBP52 is in fact a useful and reliable biomarker of prostatecancer.^(3, 7)

The biological and physiological function of FKBP52 rendered it as apotential drug target for prostate cancer treatment.^(2-3, 7-9)

However, no computational investigation of FKBP52 has beenreported.^(3-5, 7) Virtual screening (VS) has been extensively reviewedin the literature¹⁰⁻¹⁵, which refers to the application of computationalalgorithms and models for the identification of novel bioactivecompounds. For billion compounds of virtual screening libraries, VSprovides a complementary strategy to the conventional HTS¹⁵⁻¹⁹ inpharmaceutical industry.²⁰ Although the HTS technology was employed forthe development of many drug candidates, the VS approach is particularlyvaluable and practical for hit and lead discovery in academicorganizations or small biotechnology companies, because the large scaleHTS is not encouraged due to the demanding cost of resources andtime.¹³⁻¹⁴. In particular, docking and pharmacophore-based searchingtechnologies have advanced considerably and have become essential toolsin lead discovery and lead optimization of drug discovery^(13, 21-23)The scope of VS can be divided into Ligand-Based Virtual Screening(LBVS) and Structure-Based Virtual Screening (SBVS)²⁴⁻²⁵ both LBVS andSBVS technologies may accelerate the process of drug discovery.²²

LBVS is based on the fact that similar compounds should have similarproperties. The similarity of compounds to an active query compoundagainst a particular target is evaluated by the desired properties ofthe query compound.²⁶ Pharmacophore modelling²⁷, similarity search²⁶,fingerprint search²⁸, 3D-shape similarity search²⁹ are importanttechniques in LBVS.

Pharmacophore modelling is to identify the common features of a set ofknown active compounds of a biological target, which can be used as afilter to shrink down the large virtual compound libraries for furtherhit selection.³⁰ Recent development of structure-based pharmacophoremodel can also be created by overlapping the predicted binding poses ofsmall molecules docked to a biological target²⁴, the common bindinginteractions between the docked ligands and residues of the binding sitecan be easily identified and visualized.³¹

Similarity search characterizes objects as feature vectors inhigh-dimensional spaces.²⁶ Essentially, the query compound is submittedto a search engine and the search returns compounds similar to thequery.^(22, 26) It has recently gained considerable interest because ofits high performance in screening large compound databases.^(22, 26) Thesimilarity between two compounds is measured by distance functionbetween their feature vectors, and the similarity search outputs thecompounds that are nearest to the query compound in high-dimensionalspaces.²⁶

Fingerprint search finds similar molecules by comparison of thefingerprint bits^(28, 32) to a query compound. Fingerprint is simply asequence of bits, each of which represents a specific piece of thecompound.^(28, 32) The bits of a compound's fingerprint are based onsub-structure keys, topological or path, circular, pharmacophore, orSMILES^(28, 32), which are quantifiable to evaluate molecularsimilarity.^(28, 32)

Besides the above LBVS methods, quantitative structure-activityrelationship (QSAR) approach is an important methodology³³ in medicinalchemistry. CoMFA and CoMSIA methodology³⁴⁻³⁶ is an attractive technologyin 3D-QSAR approach that operates on 3D descriptors and PLS. CoMFA andCoMSIA techniques are commonly used in drug discovery by evaluatingcommon features that are important for ligand binding to a drugtarget.³⁷⁻³⁸ CoMSIA is an extension of the CoMFA on the assumption thatchanges in binding affinities of ligands correlate to the changes inmolecular properties represented by fields.³⁸ They differ only in theimplementation of the fields.³⁹⁻⁴⁰

In CoMFA and CoMSIA, a group of structurally aligned molecules arerepresented by their molecular property fields that are evaluatedbetween a probe atom and each molecule at regularly spaced intervals ona grid. CoMFA calculates steric fields using Lennard-Jones potential andelectrostatic fields using a Coulomb potential, while CoMSIA calculatesfields of steric, electrostatic, hydrophobic, hydrogen bond donor andhydrogen bond acceptor to account for the major contributions to ligandbinding.³⁹⁻⁴¹ CoMFA and CoMSIA do the systematic sampling of those fielddifferences to produce molecular descriptors well-suited forQSAR.^(38, 40, 42)

Normally, the relevant activity data can be retrieved for developingligand-based QSAR model, which can be applied for VS hit selection orfor lead optimization.²⁴ A large number of known inhibitors are curatedin public accessible databases such as ChEMBL⁴³, BindingDB⁴⁴, Reaxys⁴⁵or PubChem⁴⁶. However, beside the availability of chemical data inliterature^(40, 47), the quality of the primary activity data affectsthe performances of QSAR models the most.³³

In SBVS, docking is the core technology, which is commonly used fromscreening large chemical libraries of millions of compounds.¹⁶ The aimof docking is to predict the correct binding poses of compounds in thebinding site of a target protein and to rank the binding affinitiesprecisely.⁴⁸ The binding poses of a compound in an active site aregenerated by the docking algorithms and ranked by the score functions,by which the resulting docking score should theoretically correlate toits affinity of the receptor site.^(15, 44, 49-51) Docking needs3-dimensional protein structure to predict how the compounds should bindto the active site.⁵² The hit selection after docking can be assisted byemploying a structure-based pharmacophore model as a filter, by whichcompounds without required binding features in the active site arerejected.⁵² The quality of virtual screening may simply be measured byenrichment factor⁵² using the confirmed number of VS hits in thescreening assays.^(41, 49)

All the above methodologies are very applicable and contribute to thedrug discovery tremendously.^(13, 15-16, 26, 43, 53) And newligand-based and structure-based computational technologies for drugdesign and development are emerging from many research groups across theworld.^(10, 22, 49, 54-55) The LBVS methods are generally very fast andcomputationally much cheaper than the docking method of SBVS^(32, 52)and can make hit selection from large compound databases rational andefficient.⁵²

One of the big challenges in SBVS is to rank the binding affinity ofligands accurately.^(31, 52) Docking uses scoring functions to the rankdocking poses of small molecules in protein active site; however, thequality of scoring function is empirical and still unsatisfactory interms of ranking binding affinity of different ligands.^(33, 56-61)Thus, many potential compounds would be lost just because the chosenscore function cannot rank the ligands properly.⁶¹⁻⁶³ Hence,computational drug design is still focusing on improvement of dockingprograms, score functions⁵⁶, and data fusion.^(4, 64) In this regards,CoMFA and CoMSIA and others methodologies of QSAR^(10, 24, 33, 63) mayprovide a good ranking solution by training a model from availableexperimental data. Several VS studies have reported that consensusdocking, pharmacophore filter and 3D-QSAR, such as CoMFA and CoMSIA, aregood approaches for hit selection.^(10, 24, 33, 63, 65-66)

SUMMARY

An overall goal of embodiments of the present disclosure is to developdrugs that target the FKBP52 PPIase pocket for the disruption ofproline-rich loop interactions with AR for the treatment of prostate andbreast cancer. Embodiments of the present disclosure include methodsthat use identified three molecules PC257 (ZINC3424402) Formula I, PC892(ZINC457474880) Formula II, and PC615 (ZINC161085867) Formula III. All 3molecules are readily commercially available from Enamine, (located at 1Distribution Way, Monmouth Jct., N.J. 08852, USA).

PC257: ZINC3424402;CC1=CC(C(═O)COC(═O)CC2=NNC(═O)C=3C═CC═CC23)=C(C)N1CC4COC=5C=C C═CC5O4;[2-[1-[[(3S)-2,3-Dihydro-1,4-benzodioxin-3-yl]methyl]-2,5-dimethylpyrrol-3-yl]-2-oxoethyl]2-(4-oxo-3H-phthalazin-1-yl)acetate; C27H25N3O6; “Formula I”.

PC615: ZINC161085867;CCC1=NNC(═N1)C=2C═CC═CC2NC(═O)C(C)OC=3C═CC(C#N)=CC3;2-(4-cyanophenoxy)-N-[2-(3-ethyl-1H-1,2,4-triazol-5-yl)phenyl]propenamide;C20H19N5O2; “Formula II”.

PC892: ZINC457474880;CC(C)C1=NNC(═N1)C=2C═CC═CC2NC(═O)C(C)CC=3C═NN(C)C3;2-methyl-3-(1-methyl-1H-pyrazol-4-yl)-N-{2-[3-(propan-2-yl)-1H-1.2.4-triazol-5-yl]phenyl}propenamide;C19H24N6O; “Formula III”.

An illustrative embodiment of the present disclosure provides a methodof inhibiting hormone receptor activation, comprising administering to asubject in need of hormone receptor inhibition a compound having achemical structure of Formula I

An illustrative embodiment of the present disclosure provides a methodof treating prostate cancer or breast cancer comprising administering toa subject having prostate cancer or breast cancer a compound having achemical structure of Formula I

An illustrative embodiment of the present disclosure provides a methodof inhibiting hormone receptor activation, comprising administering to asubject in need of hormone receptor inhibition a compound having achemical structure of Formula II

An illustrative embodiment of the present disclosure provides a methodof treating prostate cancer or breast cancer comprising administering toa subject having prostate cancer or breast cancer a compound having achemical structure of Formula II

An illustrative embodiment of the present disclosure provides a methodof inhibiting hormone receptor activation, comprising administering to asubject in need of hormone receptor inhibition a compound having achemical structure of Formula III

An illustrative embodiment of the present disclosure provides a methodof treating prostate cancer or breast cancer comprising administering toa subject having prostate cancer or breast cancer a compound having achemical structure of Formula III

Other objects, features and advantages of the present invention willbecome apparent from the following detailed description. It should beunderstood, however, that the detailed description and the specificexamples, while indicating preferred embodiments of the invention, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings form part of the present specification and areincluded to further demonstrate certain aspects of the presentinvention. The invention may be better understood by reference to one ormore of these drawings in combination with the detailed description ofspecific embodiments presented herein.

FIG. 1 depicts a workflow of an FKBP52 virtual screening pipeline inaccordance with an illustrative embodiment.

FIGS. 2A-2J present structures and pIC₅₀ values for FKBP52 inhibitors inaccordance with an illustrative embodiment.

FIGS. 3A-3C depict dendograms generated with HCA using each set ofdescriptors in accordance with an illustrative embodiment. (FIG. 3A)Druglike properties. (FIG. 3B) molecular PubChem fingerprint. (FIG. 3C)Distribution of compounds among training and test sets according totheir different chemical representative calculated drug-like properties,PubChem fingerprint clusters as representative of structural diversityand the pIC₅₀ range representing the biological activity.

FIG. 4 presents pIC₅₀ values and properties calculated for 42 FKBP52inhibitors from PaDEL descriptors in accordance with an illustrativeembodiment.

FIGS. 5A-5B depict an overview of the FKBP52 inhibitors in accordancewith an illustrative embodiment. (FIG. 5A) ligand from the crystalstructure of FKBP52 (PDB ID: 4LAY) used as a template for the alignment.(FIG. 5B) Final alignment of data set. Compound: From literature citedin manuscript; IC₅₀: Half maximal inhibitory concentration; pIC₅₀: −logIC₅₀; MW: Molecular weight; Log P: Partition coefficient of a moleculebetween an aqueous and lipophilic phases, normally octanol and water;nHBAcc: Number of hydrogen bond acceptor; nHBDon: Number of hydrogenbond donor; HybRatio: Characterizes molecular complexity in terms ofcarbon hybridization states; nRotB: number of rotatable bonds; TopoPSA:Topological polar surface area; Log S: Aqueous solubility; PubChemFP:PubChem fingerprint.

FIG. 6 presents statistical results for all CoMFA models obtained fromthe region focusing technique in accordance with an illustrativeembodiment. q² _(LOO): Validation coefficient using (leave-one-out);SEP: standard error of prediction; N: number of main components obtainedfrom the PLS technique; r²: regression coefficient without validation;SEE: standard non-cross validation error; S: steric contribution; E:electrostatic contribution. w=weight; d (Å)=distance between the gridpoints.

FIG. 7 presents statistical results for all CoMSIA models obtained fromthe region focusing technique in accordance with an illustrativeembodiment. q² _(LOO): Validation coefficient using (leave-one-out);SEP: standard error of prediction; N: number of main components obtainedfrom the PLS technique; r²: regression coefficient without validation;SEE: standard non-cross validation error; A: H-bond acceptorcontribution; w=weight; d (Å)=distance between the grid points.

FIG. 8 presents statistical data of the best constructed CoMFA modelsfor FKBP52 inhibitors in accordance with an illustrative embodiment. d,distance factor; w, standard deviation weight factor; q², LOOcross-validation correlation coefficient; SEV, standard error ofvalidation; N, optimal number of components; r², non-cross-validationcorrelation coefficient; SEE, standard error of estimation;dq²/dr^(2yy′), sensitivity index from the scrambling test. Fieldcontribution: S, steric; E, electrostatic.

FIG. 9 presents statistical data of the best constructed CoMSIA modelsfor FKBP52 inhibitors in accordance with an illustrative embodiment. d,distance factor; w, standard deviation weight factor; q², LOOcross-validation correlation coefficient; SEV, standard error ofvalidation; N, optimal number of components; r², non-cross-validationcorrelation coefficient; SEE, standard error of estimation;dq²/dr^(2yy′), sensitivity index from the scrambling test. Fieldcontribution: S, steric; A, Hydrogen acceptor.

FIG. 10 presents validation of the COMFA and CoMSIA models in accordancewith an illustrative embodiment. q²: LOO cross-validation correlationcoefficient; r_(pred) ²: external predictive potential of the model;RMSEP: Root-mean-square error of prediction; r_(m) ²: externalpredictive potential of the model modified

FIGS. 11A-11B and FIGS. 12A-12B present CoMFA contour maps, for stericand electrostatic terms, and CoMSIA contribution maps, highlighting theacceptor and steric contributions in accordance with an illustrativeembodiment. Contour maps showed around the compounds 36, 38 and 39.Green contours represent regions where bulky groups increase biologicalactivity while yellow contours indicate areas where bulky groupsdecrease biological activity. Favorable electrostatic contributionsrepresented in blue, while unfavorable contributions to the biologicalactivity represented in red. Favorable acceptor contributions arehighlighted in pink, while unfavorable in grey. Contour maps showedaround the compounds less active (6, 32 and 40) and most active (36, 38and 39). Green contours represent regions where bulky groups increasebiological activity while yellow contours indicate areas where bulkygroups decrease biological activity in steric contribution.

FIGS. 13A-13B depict plots of leverage versus studentized residuals for(FIG. 13A) CoMFA and (FIG. 13B) CoMSIA: black dots represent trainingset and black triangles represent test set in accordance with anillustrative embodiment.

FIGS. 14A-14C depict experimental and predicted values of pIC₅₀ for thetraining and test sets in accordance with an illustrative embodiment.(FIG. 14A) CoMFA model, (FIG. 14B) CoMSIA model and (FIG. 14C) predictedby CoMSIA; black dots represent training set and grey dots representstest set.

FIG. 15 presents experimental and predicted pIC₅₀ for test compounds1-22 in accordance with an illustrative embodiment.

FIG. 16 presents experimental and predicted pIC₅₀ for test compounds 2,4, 7, 11, 22, 34, 39 and 42 in accordance with an illustrativeembodiment.

FIG. 17 presents results from the cross-validation (LNO) for the CoMFAmodel and a plot obtained from robustness test-cross-validated results(LNO). n_(CV)=number of groups; q² _(CV)=average of cross-validated q²in accordance with an illustrative embodiment.

FIG. 18 presents results from the cross-validation (LNO) of the CoMSIAmodel and a plot obtained from robustness test-cross-validated results(LNO) in accordance with an illustrative embodiment. n_(CV)=number ofgroups; q² _(CV)=average of cross-validated q²

FIGS. 19A-19B depict identification of novel FKBP52-specific hitcompounds in accordance with an illustrative embodiment. Structure-baseddrug design methodology and in silico library screening was used toidentify 107 molecules targeting the FKBP52 PPIase pocket. Moleculeswere assessed for the ability to inhibit AR-mediated luciferaseexpression at a single high concentration (25 μM) in MDA-kb2 cells.Molecules that showed inhibition at 25 μM were assessed in full doseresponse curves to determine the IC50. MDA-kb2 cells were treated with200 pM DHT with a range of derivative concentrations. Molecules in thelow μM range will be tested in GR-, PR- and ER-Mediated luciferaseassays in order to assess GR-dependent activity, PR-dependent activityand to test the effects of ER-regulated activity. A detailed evaluationof all candidate molecules will be tested in multiple cellular andanimal models of prostate cancer.

FIGS. 20A-20D depict PC257 (ZINC3424402) which inhibits FKBP52-SpecificAR, GR and ER-Mediated Activity in accordance with an illustrativeembodiment. (FIG. 20A) An in silico screen lead to 107 lead moleculesfor functional screening that lead to an initial hit molecule PC257(ZINC3424402). (FIG. 20B) MDA-kb2 cells expressing a stably AR- andGR-response luciferase reporter was treated with 200 pM DHT with a rangeof PC257 (ZINC3424402) concentrations (0, 0.01, 0.1, 1, 10, 25, 50, and100 uM) for 16-18 hours in order to test for AR-dependent activity. Thegraphs represent an average of 4 independent receptor mediatedluciferase receptor experiments. (FIG. 20C) MDA-kb2 cells expressing astably AR- and GR-responsive luciferase reporter was treated with 50 nMDEX with a range of PC257 (ZINC3424402) concentrations (0, 0.01, 0.1, 1,10, 25, 50, and 100 uM) for 16-18 hours in order to test forGR-dependent activity. The graphs represent an average of 4 independentreceptor mediated luciferase receptor experiments. (FIG. 20D) T47D-KBluccells express ERα and ERβ, cells were treated with 10 pM E2 with asingle high dose of 100 uM PC257 (ZINC3424402) and vehicle control for16-18 hours in order to test for effects on ER-regulated activity.

FIG. 21 depicts that PC257 specifically abrogates AR, GR andPR-dependent reporter gene expression in accordance with an illustrativeembodiment.

FIG. 22 depicts that PC257 preferentially targets FKBP52-regulatedreceptor activity in accordance with an illustrative embodiment.

FIGS. 23A-23C depict that PC257 abrogates endogenous AR-dependent geneexpression in accordance with an illustrative embodiment.

FIG. 24 depicts that PC257 Blocks Androgen-Dependent AR NuclearTranslocation in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

Broader Scale Screen in Silico Screen for Fkbp52 Inhibitors

While the targeting of the FKBP52 regulatory surface on AR is apromising therapeutic strategy that allows for AR-specific targeting,direct targeting of FKBP52 offers a number of advantages over MJC13 thatwould lead to a more potent and effective drug. First, the AR BF3surface represents a less than ideal drug binding site, and, as aresult, Applicants have only been able to achieve effective drugconcentrations in the low micromolar range. In contrast, the FKBP52PPIase pocket not only represents an ideal hydrophobic drug bindingpocket, but the FKBP PPIase pocket is a known ‘druggable target’ as theimmunosuppressive drug Tacrolimus is already FDA approved for use in theclinic. Also, given the conservation within the FKBP PPIase pocket,drugs targeting the FKBP52 PPIase pocket would likely target FKBP52 andthe closely related FKBP51 protein simultaneously. While FKBP52, but notFKBP51, is largely considered the relevant steroid hormone receptorregulator, more recent evidence suggests that both FKBP51 and FKBP52 arepositive regulators of AR in prostate cancer cells. In addition, FKBP52is a known positive regulator of AR, GR and PR, and the direct targetingof FKBP52 would target the activity of all three receptorssimultaneously. Increasing evidence suggests that many factors (e.g.growth factors, cytokines, and angiogenic factors) implicated inprostate cancer progression are targets of the GR signaling pathway. Inaddition, recent evidence suggests that GR signaling confers resistanceto current antiandrogen treatments. While very little work has been doneto characterize a role for PR in prostate cancer, data suggests that PRexpression is elevated in metastatic disease, and that PR antagonist arepotential treatments for prostate cancer. Finally, based on preliminarydata discussed below, targeting FKBP52 proline-rich loop interactionswill abrogate β-catenin interaction with AR. Thus, the direct targetingof FKBP52 with small molecules will lead to a more potent drug with thepotential to simultaneously hit a variety of targets known to have, orsuspected of having, a role in prostate cancer.

Applicants have conducted a large scale virtual screening using thecrystal structure of FKBP52 for the novel hit discovery. A FKBP52virtual screening pipeline is shown in FIG. 1 . A workflow begins withdatabase 110 and proceeds to docking analysis 120. This is followed witha Ph4 filter 130 and then QSAR model 140 analysis resulting in hits 150.However, a high quality ranking tool is needed for good hit selection.Here, Applicants are reporting CoMFA and CoMSIA models for rankingcompounds of a FKBP52 virtual screening. In addition, Applicants arereporting the three hit molecules identified in the screen. In addition,Applicants show that the most potent hit, PC257, selectively inhibitsthe steroid hormone receptors regulated by FKBP52.

CoMFA and CoMSIA Models of FKBP52

Dataset collection: Forty-two inhibitors of pipecolate sulfonamides ofthe FKBP52 dataset were selected to generate CoMFA and CoMSIA models(FIGS. 2A-2J). Biological activity data were converted into pIC₅₀ (−logIC₅₀) values, where the IC₅₀ of FKBP52 inhibition is represented asmolar values (FIGS. 2A-2J).

Compounds were grouped according to molecular structural diversity,drug-like properties and range of biological activity. Molecularstructural diversity and drug-like properties were clustered usinghierarchical cluster analysis (HCA) with the complete linkage method andEuclidian distance implemented in Chemoface. Compounds were separatedinto different groups according to their biological activity range inlog unit (FIGS. 3A-3C). Molecular structural information was encodedfrom PubChem fingerprints (PF), while drug-like properties includedescriptors of Log P, number of H-bond acceptors (HBA) and donors (HBD),topological surface area (TPSA), number of rotatable bonds (nRot) andmolecular weight (MW) (FIG. 4 ). PF and descriptors were determinedusing PaDel descriptors and all data were normalized before HCA.

The FKBP52 dataset compounds were randomly separated into a training setof 34 compounds and a test set of 8 compounds respectively, which canrepresent each cluster in the total set as shown in FIGS. 3A-3C. Theprotonation, ionization and minimization, and flexible alignment of thecompound structures were subsequently processed using MOE suite ofprograms.

Generating the FKBP52 CoMFA and CoMSIA Models

Dataset and alignment: Molecular alignment is the critical step of CoMFAand CoMSIA modelling because the three-dimensional descriptors areevaluated based on a lattice grid. The alignment of the inhibitors ofthe FKBP52 dataset indicates the importance of the three important rings(FIG. 5A). Ring 1 is a part of pipecolate group with a system of twoH-bond donor and three H-bond acceptor which in the dataset the carbonatom between oxygen (orange arrow) was substituted by a sulfur atom(FIG. 5B). The other two aromatic ring are important for hydrophobiccontacts (FIG. 5B) in the FKBP52 active site.

Models were generated using comparative molecular fields' analysis(CoMFA) and comparative molecular similarity indices analysis (CoMSIA)after alignment, respectively, using the partial least-squares (PLS)regression method implemented in Sybyl8.1 from the training set.Seventeen models of CoMFA (FIG. 6 ) and CoMSIA (FIG. 7 ) respectivelywere inferred by varying the standard parameter settings.

Normally, the quality of the models can be evaluated by correlationcoefficients: (q² and cross-validation: r²), number of principalcomponents (PC) and others parameters such as standard error estimate(SEE) and contribution of force fields. The optimal CoMFA and CoMSIAmodels are the ones with minimal PC determined by cross-validation PLSregression, which are used to generate the contour maps. Theintermediate models were inferred by varying the standard parametersettings as weight (0.3 to 1.5) and distance (1 to 4 Å) between the gridpoints. A positively charged spa carbon was used as the probe atom tocalculate molecular interaction fields (CoMFA), and a positively chargedspa hybridized carbon probe atom to calculate a range of differentsimilarity indices (CoMSIA); and the molecular alignment of training setmolecules with an initial grid spacing of 2 Å and an energy cut-off of30 kcal/mol was used to generate the CoMFA and CoMSIA models. Themolecular interaction field was calculated using the probe atom, and thesteric and electrostatic interactions with training set compounds werecalculated using Lennard-Jones and Coulomb energy terms of the CoMFAmodel. The different combinations of similarity indices of steric,electrostatic, hydrophobic, H-bond donor and H-bond acceptor werecalculated in the CoMSIA model, and the best combination for the bestmodel was determined when the highest q_(LOO) ² among the pairs ofindices was further optimized using focus approach that changes eitherthe grid spacing from 1 to 4 Å by a step of 0.5 multiplied by theoriginal distance or the weight factor from 0.3 to 1.5 by a step of 0.2multiplied by the standard deviation (SD) of the original model.

Varied combinations of weight factor and grid spacing were employed togenerate the intermediate models which were ranked by Q_(LOO) ² valuesto obtain the best model. The maximum number of principal components(PCs) used in both the CoMFA and the CoMSIA models respected the size ofthe dataset (42 compounds) that each intermediate model takes the leastnumber of PCs sufficient to explain the variability of the system (FIGS.6 and 7 ).

The best models of CoMFA and CoMSIA respectively were selected by theinternal robustness (q_(LOO) ²>0.6) and external robustness (Q_(F2) ²and Q_(F3) ²>0.7), which were used to generate contribution and contourmaps for the most and least active and selective compounds. Additionalexternal validation metrics of r_(m) ², which compares the correlationcoefficients in the prediction of the test set when passing through theorigin (r₀ ²) were evaluated to assess the model's predictability.Detailed description of these metrics can be found in a comprehensivereview. The sensitivity index (dq²/dr^(2yy′)) was generated by 50 runsof progressive scrambling CoMFA and CoMSIA, the values of what should bebetween 0.8 and 1.2 (FIGS. 8 and 9 ). Applicability domain in FIGS.13A-13B showed that 93% of the training and test set compounds areinside the predictability domain of the left-bottom dashed-linedquadrant of leverage and studentized residual.

Validation of Models

The selected CoMFA and CoMSIA models need to be cross-validated for theactivity prediction of new compounds such as filter in virtualscreening. CoMFA have steric (S) and electrostatic (E) fields whileCoMSIA presents additional contributions of hydrogen bonds (donor (D)and acceptor (A)) and hydrophobic (H) fields, which provide moreinformation about structural modification. In relation to force fieldscalculated by CoMFA and CoMSIA and to the combination of CoMFA andCoMSIA, the CoMFA model and CoMSIA model should be built by partialleast square (PLS) and validated by cross validation.

Normally, the optimal models are determined by the internal correlationcoefficients of q² and cross-validation r² and the number of principlecomponents (NP). Other parameters of a model can be calculated, such asstandard error estimate (SEE) and contribution of force fields. Thus,the best models are constructed with optimal NP by cross-validation PLSregression, which are used to generate the contour maps.

After that, the contour maps of the models are analyzed and thebiological activities of the training and test sets are predicted. Inaddition, the Y-randomization is applied to ensure the robustness of themodels to repeat the model training procedure several times by randomlyshuffling the activities in the training set. The lowest q² and r²values built with randomized activities indicate that the constructedmodels are acceptable and reliable.

It has been shown that CoMFA and CoMSIA have been used to investigatethe SAR. Therefore, Applicants have constructed CoMFA and CoMSIA modelsof FKBP52, and generated the counter maps of CoMFA and CoMSIA that canbe used for hit selection of FKBP52 VS after docking.

External Validation and Model Selection

The CoMFA and CoMSIA models were satisfactory with values within thespecifications and according to OECD guidelines. The models are good asindicated by their r_(pred) ² values of >0.7 (FIGS. 14A and 14B, as agraphical representation) and low root-mean-square error of prediction(RMSEP) rates (FIG. 10 ). Thus another external test set of 22 compoundsof pipecolate derivatives were curated and their activities werepredicted by CoMSIA model (FIG. 14C and FIG. 15 ).

The high Q_(F2) ² and Q_(F3) ² values suggest that the CoMSIA model hashigh predictability of FKBP52 inhibition. Additionally, the smalldiscrepancy between predicted and observed activity can be demonstratedby r_(m) ², which is also bigger than 0.60. Residuals were alwayssmaller than 1 and showed no correlation with predicted values (FIG. 16).

In order to test the robustness and stability of the models againstvariation of the training set composition, Applicants also performed aleave-N-out (LNO) validation (FIGS. 17 and 18 ), with cross-validationgroup numbers varying from 5 to 50 and the average q² values are biggerthan 0.8 indicating a great internal consistency.

Physicochemical Interpretation of Models

Although CoMFA and COMSIA models complement each other, CoMSIA oftenbehaves better than CoMFA, because CoMSIA model is trained with a lotmore chemical information of the training dataset. To evaluate thequality of CoMFA and CoMSIA models, it is necessary to perform bothinternal and external validation. In particular, if hydrophobic,acceptor and donor contributions are important for the dataset, it ismore likely that CoMSIA performs better than CoMFA which only considerstwo descriptors of electrostatic and steric.

The internal and external validation results in FIG. 10 of FKBP52 showedthat CoMSIA is more predictive than the CoMFA, because acceptor of thedata set (FIG. 9 ) is a strong contribution. Thus, the CoMSIA contourmap in FIGS. 11A-11B and 12A-12B should be used to evaluate differentchemical cores and substitutions to optimize or select FKBP52inhibitors.

The hydrogen acceptor maps in FIGS. 11A-11B and 12A-12B show that thebottom small purple volume of the carboxylic acid of compound 36 and ofthe dichlorophenol of compounds 38 and 39 reinforces the importance ofthe hydrogen acceptor that interacts with the active site. In contrast,the large green maps around the morpholine group of compounds 36, 38 and39 reveal the importance of the bulky hydrophobic groups; and the purplemaps highlight the favorable hydrogen acceptor contributions to thepotential hydrogen acceptors of the binding site.

The yellow maps of the phenoxyacetic acid in compound 40 and of thephenoxyacetic acid and benzothiophene of compound 6 in FIGS. 11A-11B and12A-12B suggest unfavorable steric clash with the binding site. Theyellow maps of the pyrrolidine group of compound 32 also suggests thatthe mitigation of the steric clash is critical to boost the biologicalactivity.

By the CoMFA model (FIGS. 11A-11B and 12A-12B), only small blue contourmaps present in all compounds, which suggest just favorablecontributions of the electrostatic contributions which can be explainedby a low number electrostatic contribution E of 0.207 in FIG. 8 . From acomplementary CoMSIA field analysis, substitutions of hydrogen-bondacceptor in the blue map regions should enhance biological activity.

Since the FKBP52 CoMSIA model is highly predictive, Applicants haveapplied the model to rank the docking-predicted FKBP52 binding poses ofZINC15 compounds. 106 hits were selected from the VS by the FKBP52CoMSIA ranking and visual check. Seven active compounds have beenconfirmed. The most active compound found has a IC₅₀ of approximately 1μM, which is a magnitude better than the co-crystalized ligand(IC₅₀=10.5 μM)⁹.

The CoMFA and CoMSIA results showed that CoMSIA model is more predictivethan CoMFA, which provides a good ranking tool to select FKBP52 VS hits.

Functional Screening of Virtual Hits: Identification PC257, 615, and 892as Next Generation FKBP52 Inhibitors

An in silico structure-based drug design identified 107 hits forfunctional screening. As detailed in FIGS. 19A-19B, Applicants firstscreened these molecules at a single high dose (25 μM) for inhibition ofAR activity in MDA-kb2 cell reporter assays. Any analogs that inhibitedAR activity by 75% or more were then screened on full dose responsecurves to determine the IC50. From these data, Applicants identified 3hits that displayed inhibition of AR activity in the low micromolarrange, with PC257 (ZINC3424402) being the most potent with an IC50 of 2μM. As previously mentioned FKBP52 functionally potentiates AR, GR andPR activities but does not potentiate ER. This is the result of thecochaperones proline-rich loop overhanging the PPIase catalytic pocketin the FK1 domain which is responsible for the regulation of receptoractivity.

As detailed in FIGS. 20A-20D, Applicants predict that PC257(ZINC3424402) binds to the FKBP52 pocket resulting in a conformationalchange of the proline-rich loop disrupting its interaction withreceptors, in which case displaying FKBP52-specific inhibition of AR, GRand PR but not ER activity. Applicants previously demonstrated thatPC257 (ZINC3424402) displayed inhibition of AR activity in the lowmicromolar range. As a result, Applicants wanted to show that PC257(ZINC3424402) displayed inhibition of GR activity but does not showinhibition of ER activity. Further testing will be conducted in order toshow that PC257 (ZINC3424402) inhibits PR activity. The other two hitsPC892 (ZINC457474880) and PC615 (ZINC161085867) will go through the sameprocess as PC257.

Referring to FIG. 21 , hormone-induced, receptor-dependent luciferasereporter gene expression was assessed in the presence of a range ofPC257 concentrations for androgen receptor (AR) and glucocorticoidreceptor (GR) in MDAkb2 cells, and for progesterone receptor (PR) andestrogen receptor (ER) in T47D cells. The IC50 values for AR, GR and PRinhibition are shown. These data indicate that PC257 specificallyabrogates AR, GR and PR activity, which are known to be regulated byFKBP52, but has no inhibitory activity on ER; a receptor that is notregulated by FKBP52. This strongly suggests that PC257 directly targetsthe FKBP52 protein.

Referring to FIG. 22 , dihydrotestosterone (DHT)-induced, androgenreceptor-dependent luciferase reporter gene expression was assessed inthe presence of a range of PC257 concentrations in the presence orabsence of exogenous FKBP52 expression in fkbp52-deficient 22Rv1, HeLa,and mouse embryonic fibroblast cells. The IC50 values are indicated.These data indicate that PC257 preferentially targets FKBP52-regulatedAR activity with increased potency. PC257 is anticipated to target thePPIase pocket, a highly conserved enzymatic pocket among the FKBP familyof proteins. Thus, it is likely that PC257 targets a variety of familymembers including FKBP51 (FKBPS), which has also been shown to regulateAR activity in some prostate cancer cell lines.

Referring to FIG. 23 , hormone-dependent FKBP51 and/or PSA proteinlevels were assessed by Western blot and densitometry in the indicatedcell lines in the presence of a range of PC257 concentrations. GAPDH wasused as a loading control and the densitometry data were normalized toGAPDH.

Referring to FIG. 24 , 22Rv1 prostate cancer cells were treated with orwithout 75 μM in the presence of hormone and AR and FKBP52 cellularlocalization was assessed by fluorescence microscopy. These dataindicate that PC257 significantly inhibits AR nuclear translocation.

All of the methods disclosed and claimed herein can be made and executedwithout undue experimentation in light of the present disclosure. Whilethe compositions and methods of this invention have been described interms of preferred embodiments, it will be apparent to those of skill inthe art that variations may be applied to the methods and in the stepsor in the sequence of steps of the method described herein withoutdeparting from the concept, spirit and scope of the invention. Morespecifically, it will be apparent that certain agents which are bothchemically and physiologically related may be substituted for the agentsdescribed herein while the same or similar results would be achieved.All such similar substitutes and modifications apparent to those skilledin the art are deemed to be within the spirit, scope and concept of theinvention as defined by the appended claims.

REFERENCES

-   1. World Health Organization (WHO). Cancer Mortality and Morbidity.-   2. Gaali, S.; Kirschner, A.; Cuboni, S.; Hartmann, J.; Kozany, C.;    Balsevich, G.; Namendorf, C.; Fernandez-Vizarra, P.; Sippel, C.;    Zannas, A. S.; Draenert, R.; Binder, E. B.; Almeida, O. F. X.;    Ruhter, G.; Uhr, M.; Schmidt, M. V.; Touma, C.; Bracher, A.; Hausch,    F., Selective inhibitors of the FK506-binding protein 51 by induced    fit. Nature Chemical Biology 2015, 11 (1), 33-+.-   3. Hong, C. Q.; Li, T.; Zhang, F.; Wu, X.; Chen, X. P.; Cui, X. J.;    Zhang, G. J.; Cui, Y. K., Elevated FKBP52 expression indicates a    poor outcome in patients with breast cancer. Oncology Letters 2017,    14 (5), 5379-5385.-   4. De Leon, J. T.; Iwai, A.; Feau, C.; Garcia, Y.; Balsiger, H. A.;    Storer, C. L.; Suro, R. M.; Garza, K. M.; Lee, S.; Kim, Y. S.; Chen,    Y.; Ning, Y. M.; Riggs, D. L.; Fletterick, R. J.; Guy, R. K.;    Trepel, J. B.; Neckers, L. M.; Cox, M. B., Targeting the regulation    of androgen receptor signaling by the heat shock protein 90    cochaperone FKBP52 in prostate cancer cells. Proceedings of the    National Academy of Sciences of the United States of America 2011,    108 (29), 11878-11883.-   5. Cox, M. B.; Storer, C. L.; Suh, J. H.; Chattopadhyay, A.;    Fletterick, R.; Strom, A. M.; Webb, P., FKBP52 and beta-Catenin    Directly Interact to Regulate Androgen Receptor Activity in Prostate    Cancer Cells. Endocrine Reviews 2014, 35 (3).-   6. Stope, M. B.; Burchardt, M., Re: Targeting the Regulation of    Androgen Receptor Signaling by the Heat Shock Protein 90 Cochaperone    FKBP52 in Prostate Cancer Cells. European Urology 2012, 62 (5),    931-932.-   7. Xie, H.; Ekpenyong, O., Early development of GMC1, a novel    molecule targeting FKBP52 for the treatment of hormone-refractory    prostate cancer. Cancer Research 2017, 77.-   8. Li, P. Y.; Ding, Y.; Wu, B. L.; Shu, C. L.; Shen, B. F.; Rao, Z.    H., Structure of the N-terminal domain of human FKBP52. Acta    Crystallographica Section D-Biological Crystallography 2003, 59,    16-22.-   9. Bracher, A.; Kozany, C; Hahle, A.; Wild, P.; Zacharias, M.;    Hausch, F., Crystal Structures of the Free and Ligand-Bound FK1-FK2    Domain Segment of FKBP52 Reveal a Flexible Inter-Domain Hinge.    Journal of Molecular Biology 2013, 425 (22), 4134-4144.-   10. Langer, T.; Hoffmann, R., Virtual screening an effective tool    for lead structure discovery. Current pharmaceutical design 2001, 7    (7), 509-527.-   11. Dey, S.; Ye, Q.; Sampalli, S., A machine learning based    intrusion detection scheme for data fusion in mobile clouds    involving heterogeneous client networks. Information Fusion 2019,    49, 205-215.-   12. Guo, H. Q.; Wang, Y. X.; He, Q. X.; Zhang, Y. P.; Hu, Y.;    Wang, Y. Q.; Lin, Z. H., In silico rational design and virtual    screening of antioxidant tripeptides based on 3D-QSAR modeling.    Journal of Molecular Structure 2019, 1193, 223-230.-   13. Cavasotto, C. N.; Adler, N. S.; Aucar, M. G., Quantum Chemical    Approaches in Structure-Based Virtual Screening and Lead    Optimization. Frontiers in Chemistry 2018, 6.-   14. Chatterjee, D.; Kaur, G.; Muradia, S.; Singh, B.; Agrewala, J.    N., ImmtorLig_DB: repertoire of virtually screened small molecules    against immune receptors to bolster host immunity. Scientific    Reports 2019, 9.-   15. Shahbaaz, M.; Nkaule, A.; Christoffels, A., Designing novel    possible kinase inhibitor derivatives as therapeutics against    Mycobacterium tuberculosis: An in silico study. Scientific Reports    2019, 9.-   16. Zhou, W. F.; Duan, M. J.; Fu, W. T.; Pang, J. P.; Tang, Q.;    Sun, H. Y.; Xu, L.; Chang, S.; Li, D.; Hou, T. J., Discovery of    Novel Androgen Receptor Ligands by Structure-based Virtual Screening    and Bioassays. Genomics Proteomics & Bioinformatics 2019, 16 (6),    416-427.-   17. Spena, C. R.; De Stefano, L.; Poli, G.; Granchi, C.; El    Boustani, M.; Ecca, F.; Grassi, G.; Grassi, M.; Canzonieri, V.;    Giordano, A.; Tuccinardi, T.; Caligiuri, I.; Rizzolio, F., Virtual    screening identifies a PIN1 inhibitor with possible antiovarian    cancer effects. Journal of Cellular Physiology 2019, 234 (9),    15708-15716.-   18. Li, H. F.; Ban, F. Q.; Dalal, K.; Leblanc, E.; Frewin, K.; Ma,    D.; Adomat, H.; Rennie, P. S.; Cherkasov, A., Discovery of    Small-Molecule Inhibitors Selectively Targeting the DNA-Binding    Domain of the Human Androgen Receptor. J Med Chem 2014, 57 (15),    6458-6467.-   19. Li, H.; Hassona, M. D. H.; Lack, N. A.; Axerio-Cilies, P.;    Leblanc, E.; Tavassoli, P.; Kanaan, N.; Frewin, K.; Singh, K.;    Adomat, H.; Boehm, K. J.; Prinz, H.; Guns, E. T.; Rennie, P. S.;    Cherkasov, A., Characterization of a New Class of Androgen Receptor    Antagonists with Potential Therapeutic Application in Advanced    Prostate Cancer. Molecular Cancer Therapeutics 2013, 12 (11),    2425-2435.-   20. Irwin, J. J.; Sterling, T.; Mysinger, M. M.; Bolstad, E. S.;    Coleman, R. G., ZINC: A Free Tool to Discover Chemistry for Biology.    Journal of Chemical Information and Modeling 2012, 52 (7),    1757-1768.-   21. Zhang, J.; Chai, H. T., Recent In Silico Research in    High-Throughput Drug Discovery and Molecular Biochemistry. Current    Topics in Medicinal Chemistry 2019, 19 (2), 103-104.-   22. Kumar, A.; Rathi, E.; Kini, S. G., E-pharmacophore modelling,    virtual screening, molecular dynamics simulations and in-silico ADME    analysis for identification of potential E6 inhibitors against    cervical cancer. Journal of Molecular Structure 2019, 1189, 299-306.-   23. Feng, K. R.; Ren, Y. J.; Li, R., Combined pharmacophore-guided    3D-QSAR, molecular docking and molecular dynamics studies for    evodiamine analogs as DNA topoisomerase I inhibitors. Journal of the    Taiwan Institute of Chemical Engineers 2017, 78, 81-95.-   24. Lionta, E.; Spyrou, G.; Vassilatis, D. K.; Cournia, Z.,    Structure-Based Virtual Screening for Drug Discovery: Principles,    Applications and Recent Advances. Current Topics in Medicinal    Chemistry 2014, 14 (16), 1923-1938.-   25. Kaushik, A. C.; Kumar, A.; Bharadwaj, S.; Chaudhary, R.; Sahi,    S., Ligand-Based Approach for In-silico Drug Designing.    Bioinformatics Techniques for Drug Discovery: Applications for    Complex Diseases 2018, 11-19.-   26. Mandi, M.; Ahmad, A. R.; Ismail, R.; Ieee In Similarity Search    Techniques in Exploratory Search: A Review, IEEE-Region-10    Conference (IEEE TENCON), IEEE Reg 10, SOUTH KOREA, October 28-31;    Ieee: IEEE Reg 10, SOUTH KOREA, 2018; pp 2193-2198.-   27. Li, S. R.; Fan, J. L.; Peng, C. K.; Chang, Y. Q.; Guo, L. X.;    Hou, J. S.; Huang, M. Q.; Wu, B. Y.; Zheng, J. X.; Lin, L. X.;    Xiao, G. K.; Chen, W. M.; Liao, G. C.; Guo, J. L.; Sun, P. H., New    molecular insights into the tyrosyl-tRNA synthase inhibitors: CoMFA,    CoMSIA analyses and molecular docking studies. Scientific Reports    2017, 7.-   28. Capecchi, A.; Awale, M.; Probst, D.; Reymond, J. L., PubChem and    ChEMBL beyond Lipinski. Molecular Informatics 2019, 38 (5).-   29. Nandy, A.; Roy, K.; Saha, A., Exploring molecular fingerprints    of selective PPAR delta agonists through comparative and validated    chemometric techniques. Sar and Qsar in Environmental Research 2015,    26 (5), 363-382.-   30. Xie, H. D.; Qiu, K. X.; Xie, X. G., Pharmacophore modeling,    virtual screening, and 3D-QSAR studies on a series of non-steroidal    aromatase inhibitors. Medicinal Chemistry Research 2015, 24 (5),    1901-1915.-   31. Kumar, V.; Krishna, S.; Siddiqi, M. I., Virtual screening    strategies: Recent advances in the identification and design of    anti-cancer agents. Methods 2015, 71, 64-70.-   32. Awale, M.; Visini, R.; Probst, D.; Arus-Pous, J.; Reymond, J.    L., Chemical Space: Big Data Challenge for Molecular Diversity.    Chimia 2017, 71 (10), 661-666.-   33. Cherkasov, A.; Muratov, E. N.; Fourches, D.; Varnek, A.; Baskin,    II; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y. C.;    Todeschini, R.; Consonni, V.; Kuz'min, V. E.; Cramer, R.; Benigni,    R.; Yang, C. H.; Rathman, J.; Terfloth, L.; Gasteiger, J.; Richard,    A.; Tropsha, A., QSAR Modeling: Where Have You Been? Where Are You    Going To? Journal of Medicinal Chemistry 2014, 57 (12), 4977-5010.-   34. Araujo, S. C.; Maltarollo, V. G.; Honorio, K. M., Computational    studies of TGF-beta RI (ALK-5) inhibitors: Analysis of the binding    interactions between ligand-receptor using 2D and 3D techniques.    European Journal of Pharmaceutical Sciences 2013, 49 (4), 542-549.-   35. Maltarollo, V. G.; Silva, D. C.; Honorio, K. M., Advanced QSAR    Studies on PPAR delta Ligands Related to Metabolic Diseases. Journal    of the Brazilian Chemical Society 2012, 23 (1), 85-U421.-   36. de Angelo, R.; Almeida, M.; de Paula, H.; Honorio, K., Studies    on the Dual Activity of EGFR and HER-2 Inhibitors Using    Structure-Based Drug Design Techniques. International journal of    molecular sciences 2018, 19 (12), 3728.-   37. Cramer, R. D.; Patterson, D. E.; Bunce, J. D., Comparative    molecular field analysis (CoMFA). 1. Effect of shape on binding of    steroids to carrier proteins. Journal of the American Chemical    Society 1988, 110 (18), 5959-5967.-   38. Kubinyi, H.; Folkers, G.; Martin, Y. C., 3D QSAR in Drug Design:    Volume 2: Ligand-Protein Interactions and Molecular Similarity.    Springer: 1998; Vol. 2.-   39. Llanos, E.; Leal, W.; Restrepo, G.; Stadler, P., Computational    approach to the history of chemical reactivity: Exploring Reaxys    database. Abstracts of Papers of the American Chemical Society 2017,    254.-   40. O'Boyle, N.; Sayle, R.; Bolton, E., PubChem as a biologics    database. Abstracts of Papers of the American Chemical Society 2017,    254.-   41. Saikia, S.; Bordoloi, M., Molecular Docking: Challenges,    Advances and its Use in Drug Discovery Perspective. Current Drug    Targets 2019, 20 (5), 501-521.-   42. Leal, F. D.; da Silva Lima, C. H.; de Alencastro, R. B.;    Castro, H. C.; Rodrigues, C. R.; Albuquerque, M. G., Hologram QSAR    Models of a Series of 6-Arylquinazolin-4-Amine Inhibitors of a New    Alzheimer's Disease Target: Dual Specificity    Tyrosine-Phosphorylation-Regulated Kinase-1A Enzyme. International    Journal of Molecular Sciences 2015, 16 (3), 5235-5253.-   43. Zhang, G. M.; Ren, Y. J., Molecular Modeling and Design Studies    of Purine Derivatives as Novel CDK2 Inhibitors. Molecules 2018, 23    (11).-   44. Alarn, S.; Khan, F., 3D-QSAR, Docking, ADME/Tox studies on    Flavone analogs reveal anticancer activity through Tankyrase    inhibition. Scientific Reports 2019, 9.-   45. Anderson, A. C., The process of structure-based drug design.    Chemistry & biology 2003, 10 (9), 787-797.-   46. Salum, L. B.; Polikarpov, I.; Andricopulo, A. D.,    Structure-Based Approach for the Study of Estrogen Receptor Binding    Affinity and Subtype Selectivity. Journal of Chemical Information    and Modeling 2008, 48 (11), 2243-2253.-   47. Kubinyi, H., QSAR and 3D QSAR in drug design Part 2:    applications and problems. Drug Discovery Today 1997, 2 (12),    538-546.-   48. Ou-Yang, S. S.; Lu, J. Y.; Kong, X. Q.; Liang, Z. J.; Luo, C.;    Jiang, H. L., Computational drug discovery. Acta Pharmacologica    Sinica 2012, 33 (9), 1131-1140.-   49. Cheng, T. J.; Li, Q. L.; Zhou, Z. G.; Wang, Y. L.; Bryant, S.    H., Structure-Based Virtual Screening for Drug Discovery: a    Problem-Centric Review. Aaps Journal 2012, 14 (1), 133-141.-   50. O'Boyle, N. M.; Liebeschuetz, J. W.; Cole, J. C., Testing    Assumptions and Hypotheses for Rescoring Success in Protein-Ligand    Docking. Journal of Chemical Information and Modeling 2009, 49 (8),    1871-1878.-   51. Svensson, F.; Karlen, A.; Skold, C., Virtual Screening Data    Fusion Using Both Structure- and Ligand-Based Methods. Journal of    Chemical Information and Modeling 2012, 52 (1), 225-232.-   52. Warren, G. L.; Do, T. D.; Kelley, B. P.; Nicholls, A.;    Warren, S. D., Essential considerations for using protein-ligand    structures in drug discovery. Drug Discovery Today 2012, 17 (23-24),    1270-1281.-   53. Gopalakrishnan, R.; Kozany, C.; Wang, Y. S.; Schneider, S.;    Hoogeland, B.; Bracher, A.; Hausch, F., Exploration of Pipecolate    Sulfonamides as Binders of the FK506-Binding Proteins 51 and 52.    Journal of Medicinal Chemistry 2012, 55 (9), 4123-4131.-   54. Bonomo, S.; Hansen, C. H.; Petrunak, E. M.; Scott, E. E.;    Styrishave, B.; Jorgensen, F. S.; Olsen, L., Promising Tools in    Prostate Cancer Research: Selective Non-Steroidal Cytochrome P450    17A1 Inhibitors. Scientific Reports 2016, 6.-   55. Nunes, C. A.; Freitas, M. P.; Pinheiro, A. C. M.; Bastos, S. C.,    Chemoface: a Novel Free User-Friendly Interface for Chemometrics.    Journal of the Brazilian Chemical Society 2012, 23 (11), 2003-2010.-   56. Vora, J.; Patel, S.; Sinha, S.; Sharma, S.; Srivastava, A.;    Chhabria, M.; Shrivastava, N., Structure based virtual screening,    3D-QSAR, molecular dynamics and ADMET studies for selection of    natural inhibitors against structural and non-structural targets of    Chikungunya. Journal of Biomolecular Structure & Dynamics 2019, 37    (12), 3150-3161.-   57. Gaudio, A. C.; Zandonade, E., Proposition, validation and    analysis of QSAR models. Quìmica Nova 2001, 24 (5), 658-671.-   58. Chirico, N.; Gramatica, P., Real External Predictivity of QSAR    Models: How To Evaluate It? Comparison of Different Validation    Criteria and Proposal of Using the Concordance Correlation    Coefficient. Journal of Chemical Information and Modeling 2011, 51    (9), 2320-2335.-   59. Caballero, J., 3D-QSAR (CoMFA and CoMSIA) and pharmacophore    (GALAHAD) studies on the differential inhibition of aldose reductase    by flavonoid compounds. Journal of Molecular Graphics & Modelling    2010, 29 (3), 363-371.-   60. Gramatica, P., Principles of QSAR models validation: internal    and external. QSAR & combinatorial science 2007, 26 (5), 694-701.-   61. Netzeva, T. I.; Worth, A. P.; Aldenberg, T.; Benigni, R.;    Cronin, M. T. D.; Gramatica, P.; Jaworska, J. S.; Kahn, S.; Klopman,    G.; Marchant, C. A.; Myatt, G.; Nikolova-Jeliazkova, N.;    Patlewicz, G. Y.; Perkins, R.; Roberts, D. W.; Schultz, T. W.;    Stanton, D. T.; van de Sandt, J. J. M.; Tong, W. D.; Veith, G.;    Yang, C. H., Current status of methods for defining the    applicability domain of (quantitative) structure-activity    relationships—The report and recommendations of ECVAM Workshop 52.    Atla-Alternatives to Laboratory Animals 2005, 33 (2), 155-173.-   62. Pick, A.; Muller, H.; Mayer, R.; Haenisch, B.; Pajeva, I. K.;    Weigt, M.; Bonisch, H.; Muller, C. E.; Wiese, M., Structure-activity    relationships of flavonoids as inhibitors of breast cancer    resistance protein (BCRP). Bioorg. Med. Chem. 2011, 19 (6),    2090-2102.-   63. Fang, Y. J.; Lu, Y. L.; Zang, X. X.; Wu, T.; Qi, X. J.; Pan, S.    Y.; Xu, X. Y., 3D-QSAR and docking studies of flavonoids as potent    Escherichia coli inhibitors. Scientific Reports 2016, 6.-   64. Zhu, X. X.; Li, Y.; Zhang, X. R.; Yuan, L.; Luo, P. H.; Gao, X.;    Tan, Z. S., Identifying Potential Dual Inhibitory Candidates for    Non-Small Cell Lung Cancer through Molecular Docking, 3D-QSAR    Pharmacophore-based Virtual Screening, Comparative Molecular Field    and Similarity Indices Analysis Modeling. Journal of Pharmaceutical    Research International 2018, 24 (3).-   65. Tan, Z.; Chen, L.; Zhang, S. X., Comprehensive Modeling and    Discovery of Mebendazole as a Novel TRAF2- and NCK-interacting    Kinase Inhibitor. Scientific Reports 2016, 6.-   66. De Benedetti, P. G.; Fanelli, F., Computational quantum    chemistry and adaptive ligand modeling in mechanistic QSAR. Drug    Discovery Today 2010, 15 (19-20), 859-866.-   67. Yap, C. W., PaDEL-Descriptor: An Open Source Software to    Calculate Molecular Descriptors and Fingerprints. Journal of    Computational Chemistry 2011, 32 (7), 1466-1474.-   68. Sybyl 8.1, Tripos Inc.-   69. Yao, K.; Liu, P. Y.; Liu, H. P.; Wei, Q. Y.; Yang, J.; Cao, P.;    Lai, Y. S., 3D-QSAR, molecular docking and molecular dynamics    simulations study of 3-pyrimidin-4-yl-oxazolidin-2-one derivatives    to explore the structure requirements of mutant IDH1 inhibitors.    Journal of Molecular Structure 2019, 1189, 187-202.-   70. Zhang, C. Z.; Zhang, H. J.; Huang, L. N. S.; Zhu, S. Y.; Xu, Y.;    Zhang, X. Q.; Schooley, R. T.; Yang, X. H.; Huang, Z. W.; An, J.,    Virtual Screening, Biological Evaluation, and 3D-QSAR Studies of New    HIV-1 Entry Inhibitors That Function via the CD4 Primary Receptor.    Molecules 2018, 23 (11).-   71. Tong, J.; Jiang, G.; Li, L.; Li, Y., Molecular Virtual Screening    Studies of Herbicidal Sulfonylurea Analogues Using Molecular Docking    and Topomer CoMFA Research. Journal of Structural Chemistry 2019, 60    (2), 210-218.-   72. Huang, L.; Zhang, X. R.; Luo, P. H.; Yuan, L.; Zhou, X. Z.; Gao,    X.; Li, L. S., QSAR Pharmacophore-based Virtual Screening, CoMFA and    CoMSIA Modeling and Molecular Docking towards Identifying Lead    Compounds for Breast Cancer Protease Inhibitors. British Journal of    Pharmaceutical Research 2017, 20 (1).

What is claimed is:
 1. A method of inhibiting hormone receptoractivation, comprising administering to a subject in need of hormonereceptor inhibition a compound having a chemical structure of Formula I


2. The method of claim 1, wherein the hormone receptor is the androgenreceptor.
 3. The method of claim 1, wherein the subject has hyperplasiaor neoplasia.
 4. The method of claim 1, wherein the subject has prostatecancer or breast cancer.
 5. The method of claim 1, further comprisingadministering chemotherapy or radiation treatments.
 6. A method oftreating prostate cancer or breast cancer comprising administering to asubject having prostate cancer or breast cancer a compound having achemical structure of Formula I


7. The method of claim 5, further comprising administering chemotherapyor radiation treatments.
 8. A method of inhibiting hormone receptoractivation, comprising administering to a subject in need of hormonereceptor inhibition a compound having a chemical structure of Formula I

and administering chemotherapy treatment.
 9. The method of claim 8,wherein the hormone receptor is the androgen receptor.
 10. The method ofclaim 8, wherein the subject has hyperplasia.
 11. The method of claim 8,wherein the subject has neoplasia.
 12. The method of claim 8, whereinthe subject has prostate cancer.
 13. The method of claim 8, wherein thesubject has breast cancer.
 14. A method of inhibiting hormone receptoractivation, comprising administering to a subject in need of hormonereceptor inhibition a compound having a chemical structure of Formula I

and administering radiation treatment.
 15. The method of claim 14,wherein the hormone receptor is the androgen receptor.
 16. The method ofclaim 14, wherein the subject has hyperplasia.
 17. The method of claim14, wherein the subject has neoplasia.
 18. The method of claim 14,wherein the subject has prostate cancer.
 19. The method of claim 14,wherein the subject has breast cancer.
 20. A method of treating prostatecancer or breast cancer comprising administering to a subject havingprostate cancer or breast cancer a compound having a chemical structureof Formula I

and administering chemotherapy treatment.
 21. A method of treatingprostate cancer or breast cancer comprising administering to a subjecthaving prostate cancer or breast cancer a compound having a chemicalstructure of Formula I

and administering radiation treatment.